Overview

Dataset statistics

Number of variables28
Number of observations135265
Missing cells88097
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.2 MiB
Average record size in memory613.6 B

Variable types

Numeric16
Categorical11
DateTime1

Alerts

barchive has constant value "1" Constant
bstock has constant value "1" Constant
indispobj has constant value "0" Constant
puobj has constant value "0" Constant
nomcli has a high cardinality: 11395 distinct values High cardinality
prenomcli has a high cardinality: 1842 distinct values High cardinality
villecli has a high cardinality: 6259 distinct values High cardinality
libobj has a high cardinality: 77 distinct values High cardinality
df_index is highly correlated with codcde and 1 other fieldsHigh correlation
cpcli is highly correlated with code_departementHigh correlation
codcde is highly correlated with df_index and 1 other fieldsHigh correlation
timbrecde is highly correlated with cheqcliHigh correlation
Nbcolis is highly correlated with ColisHigh correlation
cheqcli is highly correlated with timbrecde and 1 other fieldsHigh correlation
Colis is highly correlated with NbcolisHigh correlation
Poidsobj is highly correlated with pointsHigh correlation
points is highly correlated with PoidsobjHigh correlation
prixcond is highly correlated with cheqcliHigh correlation
annee is highly correlated with df_index and 1 other fieldsHigh correlation
code_departement is highly correlated with cpcliHigh correlation
df_index is highly correlated with codcde and 1 other fieldsHigh correlation
cpcli is highly correlated with code_departementHigh correlation
codcde is highly correlated with df_index and 1 other fieldsHigh correlation
timbrecde is highly correlated with cheqcliHigh correlation
Nbcolis is highly correlated with ColisHigh correlation
cheqcli is highly correlated with timbrecdeHigh correlation
Colis is highly correlated with NbcolisHigh correlation
annee is highly correlated with df_index and 1 other fieldsHigh correlation
code_departement is highly correlated with cpcliHigh correlation
df_index is highly correlated with codcde and 1 other fieldsHigh correlation
cpcli is highly correlated with code_departementHigh correlation
codcde is highly correlated with df_index and 1 other fieldsHigh correlation
timbrecde is highly correlated with cheqcliHigh correlation
Nbcolis is highly correlated with ColisHigh correlation
cheqcli is highly correlated with timbrecdeHigh correlation
Colis is highly correlated with NbcolisHigh correlation
Poidsobj is highly correlated with pointsHigh correlation
points is highly correlated with PoidsobjHigh correlation
annee is highly correlated with df_index and 1 other fieldsHigh correlation
code_departement is highly correlated with cpcliHigh correlation
libobj is highly correlated with puobj and 4 other fieldsHigh correlation
libcondit is highly correlated with puobj and 3 other fieldsHigh correlation
genrecli is highly correlated with puobj and 3 other fieldsHigh correlation
puobj is highly correlated with libobj and 6 other fieldsHigh correlation
bstock is highly correlated with libobj and 6 other fieldsHigh correlation
barchive is highly correlated with libobj and 6 other fieldsHigh correlation
Tailleobj is highly correlated with libobj and 4 other fieldsHigh correlation
indispobj is highly correlated with libobj and 6 other fieldsHigh correlation
df_index is highly correlated with codcli and 7 other fieldsHigh correlation
codcli is highly correlated with df_index and 2 other fieldsHigh correlation
cpcli is highly correlated with code_departementHigh correlation
codcde is highly correlated with df_index and 7 other fieldsHigh correlation
timbrecde is highly correlated with df_index and 7 other fieldsHigh correlation
Nbcolis is highly correlated with ColisHigh correlation
codobj is highly correlated with df_index and 6 other fieldsHigh correlation
qte is highly correlated with libobj and 1 other fieldsHigh correlation
Colis is highly correlated with NbcolisHigh correlation
libobj is highly correlated with df_index and 10 other fieldsHigh correlation
Tailleobj is highly correlated with df_index and 10 other fieldsHigh correlation
Poidsobj is highly correlated with timbrecde and 3 other fieldsHigh correlation
points is highly correlated with codobj and 2 other fieldsHigh correlation
libcondit is highly correlated with df_index and 8 other fieldsHigh correlation
prixcond is highly correlated with timbrecde and 3 other fieldsHigh correlation
annee is highly correlated with df_index and 7 other fieldsHigh correlation
code_departement is highly correlated with cpcliHigh correlation
Tailleobj has 86711 (64.1%) missing values Missing
qte is highly skewed (γ1 = 69.4799042) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
timbrecli has 130720 (96.6%) zeros Zeros
cheqcli has 5312 (3.9%) zeros Zeros
Poidsobj has 41283 (30.5%) zeros Zeros
points has 20480 (15.1%) zeros Zeros
prixcond has 76766 (56.8%) zeros Zeros

Reproduction

Analysis started2023-09-04 15:33:15.925876
Analysis finished2023-09-04 15:34:04.737051
Duration48.81 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct135265
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67636.33763
Minimum0
Maximum135276
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:04.825053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6763.2
Q133816
median67636
Q3101457
95-th percentile128510.8
Maximum135276
Range135276
Interquartile range (IQR)67641

Descriptive statistics

Standard deviation39051.4976
Coefficient of variation (CV)0.5773745145
Kurtosis-1.200016374
Mean67636.33763
Median Absolute Deviation (MAD)33821
Skewness4.737459577 × 10-5
Sum9148829210
Variance1525019465
MonotonicityStrictly increasing
2023-09-04T17:34:04.934052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
901761
 
< 0.1%
901901
 
< 0.1%
901891
 
< 0.1%
901881
 
< 0.1%
901871
 
< 0.1%
901861
 
< 0.1%
901851
 
< 0.1%
901841
 
< 0.1%
901831
 
< 0.1%
Other values (135255)135255
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1352761
< 0.1%
1352751
< 0.1%
1352741
< 0.1%
1352731
< 0.1%
1352721
< 0.1%
1352711
< 0.1%
1352701
< 0.1%
1352691
< 0.1%
1352681
< 0.1%
1352671
< 0.1%

codcli
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29781
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19724.40403
Minimum27
Maximum41291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:05.044050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile1347
Q18068
median19923
Q330915
95-th percentile38060
Maximum41291
Range41264
Interquartile range (IQR)22847

Descriptive statistics

Standard deviation12334.75588
Coefficient of variation (CV)0.6253550608
Kurtosis-1.339268738
Mean19724.40403
Median Absolute Deviation (MAD)11327
Skewness-0.02555644625
Sum2668021511
Variance152146202.7
MonotonicityNot monotonic
2023-09-04T17:34:05.142050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
786128
 
0.1%
23550124
 
0.1%
27059121
 
0.1%
8993106
 
0.1%
3190101
 
0.1%
13516100
 
0.1%
190399
 
0.1%
816387
 
0.1%
557686
 
0.1%
516585
 
0.1%
Other values (29771)134228
99.2%
ValueCountFrequency (%)
271
 
< 0.1%
289
 
< 0.1%
291
 
< 0.1%
322
 
< 0.1%
365
 
< 0.1%
392
 
< 0.1%
405
 
< 0.1%
4261
< 0.1%
431
 
< 0.1%
452
 
< 0.1%
ValueCountFrequency (%)
412914
< 0.1%
412873
< 0.1%
412862
< 0.1%
412851
 
< 0.1%
412842
< 0.1%
412831
 
< 0.1%
412821
 
< 0.1%
412811
 
< 0.1%
412802
< 0.1%
412794
< 0.1%

genrecli
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing725
Missing (%)0.5%
Memory size7.8 MiB
Mme
79455 
M.
25090 
M. & Mme
24610 
Melle
 
5377
Melles
 
3
Other values (2)
 
5

Length

Max length8
Median length3
Mean length3.808079382
Min length1

Characters and Unicode

Total characters512339
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMme
2nd rowMme
3rd rowMme
4th rowM.
5th rowMme

Common Values

ValueCountFrequency (%)
Mme79455
58.7%
M.25090
 
18.5%
M. & Mme24610
 
18.2%
Melle5377
 
4.0%
Melles3
 
< 0.1%
mme3
 
< 0.1%
m2
 
< 0.1%
(Missing)725
 
0.5%

Length

2023-09-04T17:34:05.242051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-09-04T17:34:05.347563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
mme104068
56.6%
m49702
27.0%
24610
 
13.4%
melle5377
 
2.9%
melles3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M159145
31.1%
e114828
22.4%
m104073
20.3%
.49700
 
9.7%
49220
 
9.6%
&24610
 
4.8%
l10760
 
2.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter229664
44.8%
Uppercase Letter159145
31.1%
Other Punctuation74310
 
14.5%
Space Separator49220
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e114828
50.0%
m104073
45.3%
l10760
 
4.7%
s3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.49700
66.9%
&24610
33.1%
Uppercase Letter
ValueCountFrequency (%)
M159145
100.0%
Space Separator
ValueCountFrequency (%)
49220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin388809
75.9%
Common123530
 
24.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
M159145
40.9%
e114828
29.5%
m104073
26.8%
l10760
 
2.8%
s3
 
< 0.1%
Common
ValueCountFrequency (%)
.49700
40.2%
49220
39.8%
&24610
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII512339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M159145
31.1%
e114828
22.4%
m104073
20.3%
.49700
 
9.7%
49220
 
9.6%
&24610
 
4.8%
l10760
 
2.1%
s3
 
< 0.1%

nomcli
Categorical

HIGH CARDINALITY

Distinct11395
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size8.3 MiB
MARIE
 
707
LAUNAY
 
400
GAUTIER
 
371
LEROY
 
370
DURAND
 
352
Other values (11390)
133065 

Length

Max length32
Median length26
Mean length6.913407016
Min length2

Characters and Unicode

Total characters935142
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2276 ?
Unique (%)1.7%

Sample

1st rowCHRETIEN
2nd rowCHRETIEN
3rd rowCHRETIEN
4th rowVERARDO
5th rowROBERT

Common Values

ValueCountFrequency (%)
MARIE707
 
0.5%
LAUNAY400
 
0.3%
GAUTIER371
 
0.3%
LEROY370
 
0.3%
DURAND352
 
0.3%
MARTIN345
 
0.3%
RICHARD343
 
0.3%
LEBRETON342
 
0.3%
DUVAL324
 
0.2%
MOREL312
 
0.2%
Other values (11385)131399
97.1%

Length

2023-09-04T17:34:05.449564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
le2490
 
1.8%
marie721
 
0.5%
launay400
 
0.3%
gautier371
 
0.3%
leroy370
 
0.3%
martin353
 
0.3%
durand352
 
0.3%
richard343
 
0.2%
lebreton342
 
0.2%
tessier327
 
0.2%
Other values (11296)132723
95.6%

Most occurring characters

ValueCountFrequency (%)
E132675
14.2%
R86735
 
9.3%
A80938
 
8.7%
L73160
 
7.8%
I64640
 
6.9%
O63226
 
6.8%
N61421
 
6.6%
U60238
 
6.4%
T45505
 
4.9%
S35563
 
3.8%
Other values (34)231041
24.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter930376
99.5%
Space Separator3607
 
0.4%
Dash Punctuation883
 
0.1%
Other Punctuation260
 
< 0.1%
Lowercase Letter16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E132675
14.3%
R86735
 
9.3%
A80938
 
8.7%
L73160
 
7.9%
I64640
 
6.9%
O63226
 
6.8%
N61421
 
6.6%
U60238
 
6.5%
T45505
 
4.9%
S35563
 
3.8%
Other values (24)226275
24.3%
Other Punctuation
ValueCountFrequency (%)
'250
96.2%
.4
 
1.5%
"4
 
1.5%
&2
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
f4
25.0%
s4
25.0%
l4
25.0%
i4
25.0%
Space Separator
ValueCountFrequency (%)
3607
100.0%
Dash Punctuation
ValueCountFrequency (%)
-883
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin930392
99.5%
Common4750
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E132675
14.3%
R86735
 
9.3%
A80938
 
8.7%
L73160
 
7.9%
I64640
 
6.9%
O63226
 
6.8%
N61421
 
6.6%
U60238
 
6.5%
T45505
 
4.9%
S35563
 
3.8%
Other values (28)226291
24.3%
Common
ValueCountFrequency (%)
3607
75.9%
-883
 
18.6%
'250
 
5.3%
.4
 
0.1%
"4
 
0.1%
&2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII934517
99.9%
None625
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E132675
14.2%
R86735
 
9.3%
A80938
 
8.7%
L73160
 
7.8%
I64640
 
6.9%
O63226
 
6.8%
N61421
 
6.6%
U60238
 
6.4%
T45505
 
4.9%
S35563
 
3.8%
Other values (26)230416
24.7%
None
ValueCountFrequency (%)
Ç405
64.8%
Ï103
 
16.5%
Î73
 
11.7%
Ü25
 
4.0%
À9
 
1.4%
Œ5
 
0.8%
Û3
 
0.5%
Á2
 
0.3%

prenomcli
Categorical

HIGH CARDINALITY

Distinct1842
Distinct (%)1.4%
Missing324
Missing (%)0.2%
Memory size8.3 MiB
Michel
 
3068
Monique
 
2384
Jacqueline
 
2036
Francoise
 
2007
Annick
 
1960
Other values (1837)
123486 

Length

Max length29
Median length26
Mean length7.358534471
Min length2

Characters and Unicode

Total characters992968
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique364 ?
Unique (%)0.3%

Sample

1st rowDaniel
2nd rowDaniel
3rd rowDaniel
4th rowAnthony
5th rowYvonne

Common Values

ValueCountFrequency (%)
Michel3068
 
2.3%
Monique2384
 
1.8%
Jacqueline2036
 
1.5%
Francoise2007
 
1.5%
Annick1960
 
1.4%
Nicole1912
 
1.4%
Claude1877
 
1.4%
Daniel1602
 
1.2%
Therese1600
 
1.2%
Martine1545
 
1.1%
Other values (1832)114950
85.0%

Length

2023-09-04T17:34:05.551562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michel3210
 
2.3%
monique2443
 
1.8%
jacqueline2055
 
1.5%
francoise2055
 
1.5%
nicole1984
 
1.4%
annick1971
 
1.4%
claude1944
 
1.4%
et1877
 
1.3%
daniel1622
 
1.2%
therese1614
 
1.2%
Other values (1495)118283
85.1%

Most occurring characters

ValueCountFrequency (%)
e186712
18.8%
i97207
 
9.8%
n87576
 
8.8%
a82014
 
8.3%
r63364
 
6.4%
l59851
 
6.0%
t43005
 
4.3%
o30317
 
3.1%
c29110
 
2.9%
u26714
 
2.7%
Other values (50)287098
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter829837
83.6%
Uppercase Letter147941
 
14.9%
Dash Punctuation10876
 
1.1%
Space Separator4117
 
0.4%
Other Punctuation197
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e186712
22.5%
i97207
11.7%
n87576
10.6%
a82014
9.9%
r63364
 
7.6%
l59851
 
7.2%
t43005
 
5.2%
o30317
 
3.7%
c29110
 
3.5%
u26714
 
3.2%
Other values (18)123967
14.9%
Uppercase Letter
ValueCountFrequency (%)
M25216
17.0%
J16354
11.1%
C16031
10.8%
A11823
 
8.0%
P8265
 
5.6%
G8189
 
5.5%
S6878
 
4.6%
R6834
 
4.6%
D6784
 
4.6%
L6534
 
4.4%
Other values (16)35033
23.7%
Other Punctuation
ValueCountFrequency (%)
&137
69.5%
.43
 
21.8%
,16
 
8.1%
'1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
-10876
100.0%
Space Separator
ValueCountFrequency (%)
4117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin977778
98.5%
Common15190
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e186712
19.1%
i97207
 
9.9%
n87576
 
9.0%
a82014
 
8.4%
r63364
 
6.5%
l59851
 
6.1%
t43005
 
4.4%
o30317
 
3.1%
c29110
 
3.0%
u26714
 
2.7%
Other values (44)271908
27.8%
Common
ValueCountFrequency (%)
-10876
71.6%
4117
 
27.1%
&137
 
0.9%
.43
 
0.3%
,16
 
0.1%
'1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII992965
> 99.9%
None3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e186712
18.8%
i97207
 
9.8%
n87576
 
8.8%
a82014
 
8.3%
r63364
 
6.4%
l59851
 
6.0%
t43005
 
4.3%
o30317
 
3.1%
c29110
 
2.9%
u26714
 
2.7%
Other values (48)287095
28.9%
None
ValueCountFrequency (%)
œ2
66.7%
á1
33.3%

cpcli
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2276
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48182.37992
Minimum1120
Maximum95870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:05.654562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1120
5-th percentile14200
Q128800
median50690
Q361350
95-th percentile76400
Maximum95870
Range94750
Interquartile range (IQR)32550

Descriptive statistics

Standard deviation20452.97062
Coefficient of variation (CV)0.4244906675
Kurtosis-0.8093470195
Mean48182.37992
Median Absolute Deviation (MAD)11450
Skewness-0.3038085864
Sum6517389620
Variance418324007
MonotonicityNot monotonic
2023-09-04T17:34:05.754563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611003282
 
2.4%
140001537
 
1.1%
610001503
 
1.1%
618001484
 
1.1%
720001382
 
1.0%
617001256
 
0.9%
530001119
 
0.8%
616001057
 
0.8%
14500977
 
0.7%
61250977
 
0.7%
Other values (2266)120691
89.2%
ValueCountFrequency (%)
11201
 
< 0.1%
14301
 
< 0.1%
14743
 
< 0.1%
20003
 
< 0.1%
210025
< 0.1%
211014
< 0.1%
212015
< 0.1%
213016
< 0.1%
21406
 
< 0.1%
21905
 
< 0.1%
ValueCountFrequency (%)
958702
 
< 0.1%
958301
 
< 0.1%
958009
< 0.1%
9564011
< 0.1%
956302
 
< 0.1%
956104
 
< 0.1%
956009
< 0.1%
955501
 
< 0.1%
955402
 
< 0.1%
955206
< 0.1%

villecli
Categorical

HIGH CARDINALITY

Distinct6259
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
LE MANS
 
2229
CAEN
 
1537
LAVAL
 
1119
FLERS
 
1030
VIRE NORMANDIE
 
938
Other values (6254)
128412 

Length

Max length35
Median length27
Mean length12.378361
Min length2

Characters and Unicode

Total characters1674359
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique807 ?
Unique (%)0.6%

Sample

1st rowBOURGUEBUS
2nd rowBOURGUEBUS
3rd rowBOURGUEBUS
4th rowSAINT MALO
5th rowALENCON

Common Values

ValueCountFrequency (%)
LE MANS2229
 
1.6%
CAEN1537
 
1.1%
LAVAL1119
 
0.8%
FLERS1030
 
0.8%
VIRE NORMANDIE938
 
0.7%
CHERBOURG EN COTENTIN911
 
0.7%
ALENCON760
 
0.6%
RENNES694
 
0.5%
ARGENTAN645
 
0.5%
LE HAVRE549
 
0.4%
Other values (6249)124853
92.3%

Length

2023-09-04T17:34:05.862591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
le12022
 
4.3%
sur10883
 
3.9%
st10521
 
3.8%
la9785
 
3.5%
saint9104
 
3.3%
en5909
 
2.1%
de5726
 
2.1%
les5081
 
1.8%
du3748
 
1.4%
mans2425
 
0.9%
Other values (5509)202263
72.9%

Most occurring characters

ValueCountFrequency (%)
E236625
14.1%
142229
 
8.5%
A132109
 
7.9%
L131594
 
7.9%
R121809
 
7.3%
N121435
 
7.3%
S112143
 
6.7%
I101506
 
6.1%
O86745
 
5.2%
U75704
 
4.5%
Other values (45)412460
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1527546
91.2%
Space Separator142229
 
8.5%
Other Punctuation4451
 
0.3%
Lowercase Letter111
 
< 0.1%
Decimal Number22
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E236625
15.5%
A132109
 
8.6%
L131594
 
8.6%
R121809
 
8.0%
N121435
 
7.9%
S112143
 
7.3%
I101506
 
6.6%
O86745
 
5.7%
U75704
 
5.0%
T72771
 
4.8%
Other values (20)335105
21.9%
Lowercase Letter
ValueCountFrequency (%)
e26
23.4%
n15
13.5%
l12
10.8%
i11
9.9%
o9
 
8.1%
u8
 
7.2%
r6
 
5.4%
v5
 
4.5%
s5
 
4.5%
b4
 
3.6%
Other values (5)10
 
9.0%
Decimal Number
ValueCountFrequency (%)
710
45.5%
23
 
13.6%
12
 
9.1%
92
 
9.1%
62
 
9.1%
51
 
4.5%
01
 
4.5%
41
 
4.5%
Space Separator
ValueCountFrequency (%)
142229
100.0%
Other Punctuation
ValueCountFrequency (%)
'4451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1527657
91.2%
Common146702
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E236625
15.5%
A132109
 
8.6%
L131594
 
8.6%
R121809
 
8.0%
N121435
 
7.9%
S112143
 
7.3%
I101506
 
6.6%
O86745
 
5.7%
U75704
 
5.0%
T72771
 
4.8%
Other values (35)335216
21.9%
Common
ValueCountFrequency (%)
142229
97.0%
'4451
 
3.0%
710
 
< 0.1%
23
 
< 0.1%
12
 
< 0.1%
92
 
< 0.1%
62
 
< 0.1%
51
 
< 0.1%
01
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1673998
> 99.9%
None361
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E236625
14.1%
142229
 
8.5%
A132109
 
7.9%
L131594
 
7.9%
R121809
 
7.3%
N121435
 
7.3%
S112143
 
6.7%
I101506
 
6.1%
O86745
 
5.2%
U75704
 
4.5%
Other values (41)412099
24.6%
None
ValueCountFrequency (%)
Û175
48.5%
Ç136
37.7%
Î48
 
13.3%
Œ2
 
0.6%

codcde
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct70892
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56834.25367
Minimum478
Maximum90048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:05.965563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum478
5-th percentile20680.2
Q142027
median58648
Q372837
95-th percentile86706
Maximum90048
Range89570
Interquartile range (IQR)30810

Descriptive statistics

Standard deviation20267.2519
Coefficient of variation (CV)0.3566027632
Kurtosis-0.9251725864
Mean56834.25367
Median Absolute Deviation (MAD)15178
Skewness-0.2881391328
Sum7687685322
Variance410761499.6
MonotonicityNot monotonic
2023-09-04T17:34:06.063562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8813020
 
< 0.1%
8859918
 
< 0.1%
7865117
 
< 0.1%
7611514
 
< 0.1%
8416114
 
< 0.1%
7746214
 
< 0.1%
8449714
 
< 0.1%
8619913
 
< 0.1%
8057913
 
< 0.1%
7438613
 
< 0.1%
Other values (70882)135115
99.9%
ValueCountFrequency (%)
4783
< 0.1%
7542
< 0.1%
13861
 
< 0.1%
19381
 
< 0.1%
19632
< 0.1%
21411
 
< 0.1%
29901
 
< 0.1%
45041
 
< 0.1%
48592
< 0.1%
54651
 
< 0.1%
ValueCountFrequency (%)
900482
< 0.1%
900472
< 0.1%
900453
< 0.1%
900441
 
< 0.1%
900432
< 0.1%
900422
< 0.1%
900412
< 0.1%
900401
 
< 0.1%
900391
 
< 0.1%
900381
 
< 0.1%

datcde
Date

Distinct3935
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Minimum2004-10-22 00:00:00
Maximum2021-07-26 00:00:00
2023-09-04T17:34:06.173562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:06.283072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

timbrecli
Real number (ℝ≥0)

ZEROS

Distinct412
Distinct (%)0.3%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1131842172
Minimum0
Maximum20.25
Zeros130720
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:06.394071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20.25
Range20.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.760094766
Coefficient of variation (CV)6.715554385
Kurtosis103.8980789
Mean0.1131842172
Median Absolute Deviation (MAD)0
Skewness8.926313061
Sum15309.4104
Variance0.5777440533
MonotonicityNot monotonic
2023-09-04T17:34:06.499072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130720
96.6%
0.77227
 
0.2%
4.24184
 
0.1%
1.06164
 
0.1%
1.59135
 
0.1%
1.45134
 
0.1%
5.4124
 
0.1%
5.3123
 
0.1%
4.32118
 
0.1%
1.08109
 
0.1%
Other values (402)3223
 
2.4%
ValueCountFrequency (%)
0130720
96.6%
0.11
 
< 0.1%
0.21
 
< 0.1%
0.311
 
< 0.1%
0.481
 
< 0.1%
0.492
 
< 0.1%
0.511
 
< 0.1%
0.5323
 
< 0.1%
0.5418
 
< 0.1%
0.5514
 
< 0.1%
ValueCountFrequency (%)
20.251
 
< 0.1%
201
 
< 0.1%
19.32
 
< 0.1%
18.551
 
< 0.1%
18.42
 
< 0.1%
16.533
< 0.1%
16.431
 
< 0.1%
15.93
< 0.1%
15.21
 
< 0.1%
15.125
< 0.1%

timbrecde
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct126
Distinct (%)0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.076797776
Minimum0.64
Maximum17.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:06.610072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.64
5-th percentile1.5
Q12.75
median5.3
Q36.5
95-th percentile8.7
Maximum17.9
Range17.26
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.356470545
Coefficient of variation (CV)0.4641647449
Kurtosis-0.2749414736
Mean5.076797776
Median Absolute Deviation (MAD)1.4
Skewness0.119615601
Sum686667.36
Variance5.552953428
MonotonicityNot monotonic
2023-09-04T17:34:06.712071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.359734
 
7.2%
1.59575
 
7.1%
4.68168
 
6.0%
2.57265
 
5.4%
5.86626
 
4.9%
5.66503
 
4.8%
1.656446
 
4.8%
6.55505
 
4.1%
5.94927
 
3.6%
5.13772
 
2.8%
Other values (116)66735
49.3%
ValueCountFrequency (%)
0.64206
 
0.2%
0.77397
 
0.3%
1.42493
 
1.8%
1.45261
 
0.2%
1.59575
7.1%
1.656446
4.8%
1.82040
 
1.5%
1.91704
 
1.3%
2.094
 
< 0.1%
2.11380
 
1.0%
ValueCountFrequency (%)
17.910
 
< 0.1%
17.14
 
< 0.1%
16.36
 
< 0.1%
16.253
 
< 0.1%
15.52
 
< 0.1%
15.430
< 0.1%
14.644
< 0.1%
14.2537
< 0.1%
14.118
< 0.1%
13.734
< 0.1%

Nbcolis
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.017485232
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:06.799071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1518708722
Coefficient of variation (CV)0.1492610089
Kurtosis203.7470659
Mean1.017485232
Median Absolute Deviation (MAD)0
Skewness11.69883446
Sum137622
Variance0.02306476183
MonotonicityNot monotonic
2023-09-04T17:34:06.863073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1133183
98.5%
21851
 
1.4%
3183
 
0.1%
421
 
< 0.1%
512
 
< 0.1%
65
 
< 0.1%
72
 
< 0.1%
(Missing)8
 
< 0.1%
ValueCountFrequency (%)
1133183
98.5%
21851
 
1.4%
3183
 
0.1%
421
 
< 0.1%
512
 
< 0.1%
65
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
65
 
< 0.1%
512
 
< 0.1%
421
 
< 0.1%
3183
 
0.1%
21851
 
1.4%
1133183
98.5%

cheqcli
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct848
Distinct (%)0.6%
Missing43
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.711899439
Minimum0
Maximum180
Zeros5312
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:06.954074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q12.86
median4.6
Q36.2
95-th percentile8.7
Maximum180
Range180
Interquartile range (IQR)3.34

Descriptive statistics

Standard deviation2.601003809
Coefficient of variation (CV)0.5520074956
Kurtosis418.1100805
Mean4.711899439
Median Absolute Deviation (MAD)1.7
Skewness6.751708281
Sum637152.466
Variance6.765220815
MonotonicityNot monotonic
2023-09-04T17:34:07.063072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3512296
 
9.1%
1.57284
 
5.4%
1.45445
 
4.0%
05312
 
3.9%
5.85114
 
3.8%
5.14337
 
3.2%
4.64320
 
3.2%
6.53691
 
2.7%
4.33488
 
2.6%
5.63323
 
2.5%
Other values (838)80612
59.6%
ValueCountFrequency (%)
05312
3.9%
0.521
 
< 0.1%
0.533
 
< 0.1%
0.562
 
< 0.1%
0.571
 
< 0.1%
0.62
 
< 0.1%
0.64251
 
0.2%
0.653
 
< 0.1%
0.79
 
< 0.1%
0.753
 
< 0.1%
ValueCountFrequency (%)
1802
 
< 0.1%
1651
 
< 0.1%
74.31
 
< 0.1%
503
 
< 0.1%
47.722
 
< 0.1%
351
 
< 0.1%
201
 
< 0.1%
17.17
< 0.1%
179
< 0.1%
16.2517
< 0.1%

barchive
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
1
135265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters135265
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1135265
100.0%

Length

2023-09-04T17:34:07.157072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-09-04T17:34:07.229071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1135265
100.0%

Most occurring characters

ValueCountFrequency (%)
1135265
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number135265
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1135265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common135265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1135265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII135265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1135265
100.0%

bstock
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
1
135265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters135265
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1135265
100.0%

Length

2023-09-04T17:34:07.291071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-09-04T17:34:07.365074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1135265
100.0%

Most occurring characters

ValueCountFrequency (%)
1135265
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number135265
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1135265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common135265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1135265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII135265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1135265
100.0%

codobj
Real number (ℝ≥0)

HIGH CORRELATION

Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.61525894
Minimum20
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:07.438330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q142
median65
Q384
95-th percentile137
Maximum168
Range148
Interquartile range (IQR)42

Descriptive statistics

Standard deviation34.23426908
Coefficient of variation (CV)0.4989308444
Kurtosis-0.1948715913
Mean68.61525894
Median Absolute Deviation (MAD)23
Skewness0.7725815836
Sum9281243
Variance1171.985179
MonotonicityNot monotonic
2023-09-04T17:34:07.537327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4213126
 
9.7%
4112937
 
9.6%
8410658
 
7.9%
7110538
 
7.8%
1159272
 
6.9%
486683
 
4.9%
454994
 
3.7%
444938
 
3.7%
204852
 
3.6%
794418
 
3.3%
Other values (110)52849
39.1%
ValueCountFrequency (%)
204852
3.6%
21236
 
0.2%
22485
 
0.4%
23796
 
0.6%
242963
2.2%
25135
 
0.1%
26162
 
0.1%
27195
 
0.1%
28217
 
0.2%
29535
 
0.4%
ValueCountFrequency (%)
16833
 
< 0.1%
1671
 
< 0.1%
166242
0.2%
165232
0.2%
16353
 
< 0.1%
162132
 
0.1%
16182
 
0.1%
160419
0.3%
159251
0.2%
15853
 
< 0.1%

qte
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct27
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.442031021
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:07.633327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum300
Range299
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.436833281
Coefficient of variation (CV)0.9963955419
Kurtosis13811.63316
Mean1.442031021
Median Absolute Deviation (MAD)0
Skewness69.4799042
Sum195052
Variance2.064489877
MonotonicityNot monotonic
2023-09-04T17:34:07.713328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1105297
77.8%
217492
 
12.9%
35850
 
4.3%
42777
 
2.1%
51335
 
1.0%
61335
 
1.0%
7351
 
0.3%
8233
 
0.2%
12212
 
0.2%
10162
 
0.1%
Other values (17)218
 
0.2%
ValueCountFrequency (%)
1105297
77.8%
217492
 
12.9%
35850
 
4.3%
42777
 
2.1%
51335
 
1.0%
61335
 
1.0%
7351
 
0.3%
8233
 
0.2%
9114
 
0.1%
10162
 
0.1%
ValueCountFrequency (%)
3001
 
< 0.1%
511
 
< 0.1%
281
 
< 0.1%
262
 
< 0.1%
253
 
< 0.1%
244
 
< 0.1%
221
 
< 0.1%
2012
< 0.1%
191
 
< 0.1%
186
< 0.1%

Colis
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.017374332
Minimum0
Maximum7
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:07.790328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1515180748
Coefficient of variation (CV)0.1489305067
Kurtosis205.1927145
Mean1.017374332
Median Absolute Deviation (MAD)0
Skewness11.7271953
Sum137607
Variance0.02295772698
MonotonicityNot monotonic
2023-09-04T17:34:07.855328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1133192
98.5%
21842
 
1.4%
3181
 
0.1%
421
 
< 0.1%
512
 
< 0.1%
65
 
< 0.1%
72
 
< 0.1%
02
 
< 0.1%
(Missing)8
 
< 0.1%
ValueCountFrequency (%)
02
 
< 0.1%
1133192
98.5%
21842
 
1.4%
3181
 
0.1%
421
 
< 0.1%
512
 
< 0.1%
65
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
65
 
< 0.1%
512
 
< 0.1%
421
 
< 0.1%
3181
 
0.1%
21842
 
1.4%
1133192
98.5%
02
 
< 0.1%

libobj
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Tete de menagere
13137 
Drap de bain
12937 
Flyer
10658 
Points Bonus Fidelite
10538 
Montre
9932 
Other values (72)
78063 

Length

Max length24
Median length19
Mean length12.21370643
Min length3

Characters and Unicode

Total characters1652087
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPolo
2nd rowT-shirt Blanc
3rd rowMontre
4th rowT-shirt Blanc
5th rowTete de menagere

Common Values

ValueCountFrequency (%)
Tete de menagere13137
 
9.7%
Drap de bain12937
 
9.6%
Flyer10658
 
7.9%
Points Bonus Fidelite10538
 
7.8%
Montre9932
 
7.3%
Flyer 20149272
 
6.9%
Carte publicitaire6683
 
4.9%
T-shirt Blanc5311
 
3.9%
Jeu de cartes4852
 
3.6%
Cle USB4418
 
3.3%
Other values (67)47527
35.1%

Length

2023-09-04T17:34:07.943327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de34535
 
11.7%
flyer24037
 
8.1%
points14645
 
5.0%
tete13141
 
4.4%
menagere13141
 
4.4%
bain12939
 
4.4%
drap12939
 
4.4%
a12763
 
4.3%
bonus10540
 
3.6%
fidelite10538
 
3.6%
Other values (97)136583
46.2%

Most occurring characters

ValueCountFrequency (%)
e254492
15.4%
160536
 
9.7%
r116785
 
7.1%
t110576
 
6.7%
a104222
 
6.3%
i96276
 
5.8%
l81675
 
4.9%
n77801
 
4.7%
o64295
 
3.9%
s56839
 
3.4%
Other values (44)528590
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1248755
75.6%
Uppercase Letter192877
 
11.7%
Space Separator160536
 
9.7%
Decimal Number39085
 
2.4%
Dash Punctuation7868
 
0.5%
Math Symbol2121
 
0.1%
Other Punctuation845
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e254492
20.4%
r116785
9.4%
t110576
8.9%
a104222
8.3%
i96276
 
7.7%
l81675
 
6.5%
n77801
 
6.2%
o64295
 
5.1%
s56839
 
4.6%
u50224
 
4.0%
Other values (14)235570
18.9%
Uppercase Letter
ValueCountFrequency (%)
F36797
19.1%
T25617
13.3%
C25243
13.1%
B24191
12.5%
P19132
9.9%
S18227
9.5%
D12939
 
6.7%
M11356
 
5.9%
J4855
 
2.5%
U4650
 
2.4%
Other values (9)9870
 
5.1%
Decimal Number
ValueCountFrequency (%)
29999
25.6%
19604
24.6%
09440
24.2%
49272
23.7%
6688
 
1.8%
982
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.609
72.1%
'236
 
27.9%
Space Separator
ValueCountFrequency (%)
160536
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7868
100.0%
Math Symbol
ValueCountFrequency (%)
+2121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1441632
87.3%
Common210455
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e254492
17.7%
r116785
 
8.1%
t110576
 
7.7%
a104222
 
7.2%
i96276
 
6.7%
l81675
 
5.7%
n77801
 
5.4%
o64295
 
4.5%
s56839
 
3.9%
u50224
 
3.5%
Other values (33)428447
29.7%
Common
ValueCountFrequency (%)
160536
76.3%
29999
 
4.8%
19604
 
4.6%
09440
 
4.5%
49272
 
4.4%
-7868
 
3.7%
+2121
 
1.0%
6688
 
0.3%
.609
 
0.3%
'236
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1651715
> 99.9%
None372
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e254492
15.4%
160536
 
9.7%
r116785
 
7.1%
t110576
 
6.7%
a104222
 
6.3%
i96276
 
5.8%
l81675
 
4.9%
n77801
 
4.7%
o64295
 
3.9%
s56839
 
3.4%
Other values (43)528218
32.0%
None
ValueCountFrequency (%)
û372
100.0%

Tailleobj
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)0.1%
Missing86711
Missing (%)64.1%
Memory size5.6 MiB
Confidence
19926 
Homme
4994 
Femme
4938 
Reynolds
2963 
XL
2774 
Other values (22)
12959 

Length

Max length14
Median length10
Mean length7.043580344
Min length1

Characters and Unicode

Total characters341994
Distinct characters45
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXL
2nd rowL
3rd rowHomme
4th rowXL
5th rowConfidence

Common Values

ValueCountFrequency (%)
Confidence19926
 
14.7%
Homme4994
 
3.7%
Femme4938
 
3.7%
Reynolds2963
 
2.2%
XL2774
 
2.1%
L1883
 
1.4%
Colory1782
 
1.3%
XXL1762
 
1.3%
Verlaine1486
 
1.1%
Rouge1064
 
0.8%
Other values (17)4982
 
3.7%
(Missing)86711
64.1%

Length

2023-09-04T17:34:08.034327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
confidence19926
40.5%
homme4994
 
10.1%
femme4938
 
10.0%
reynolds2963
 
6.0%
xl2774
 
5.6%
l1883
 
3.8%
colory1782
 
3.6%
xxl1762
 
3.6%
verlaine1486
 
3.0%
rouge1064
 
2.2%
Other values (20)5636
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e63571
18.6%
n46184
13.5%
o35055
10.3%
d23515
 
6.9%
C21883
 
6.4%
i21648
 
6.3%
m20799
 
6.1%
c20098
 
5.9%
f19926
 
5.8%
X7822
 
2.3%
Other values (35)61493
18.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter282674
82.7%
Uppercase Letter55773
 
16.3%
Decimal Number2292
 
0.7%
Space Separator654
 
0.2%
Dash Punctuation548
 
0.2%
Other Punctuation53
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e63571
22.5%
n46184
16.3%
o35055
12.4%
d23515
 
8.3%
i21648
 
7.7%
m20799
 
7.4%
c20098
 
7.1%
f19926
 
7.0%
l6575
 
2.3%
r6316
 
2.2%
Other values (11)18987
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
C21883
39.2%
X7822
 
14.0%
L7159
 
12.8%
H4994
 
9.0%
F4991
 
8.9%
R4038
 
7.2%
M2001
 
3.6%
V1486
 
2.7%
B626
 
1.1%
Q274
 
0.5%
Other values (2)499
 
0.9%
Decimal Number
ValueCountFrequency (%)
1875
38.2%
2356
15.5%
8217
 
9.5%
4195
 
8.5%
9162
 
7.1%
0161
 
7.0%
5135
 
5.9%
6135
 
5.9%
756
 
2.4%
Space Separator
ValueCountFrequency (%)
654
100.0%
Dash Punctuation
ValueCountFrequency (%)
-548
100.0%
Other Punctuation
ValueCountFrequency (%)
'53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338447
99.0%
Common3547
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e63571
18.8%
n46184
13.6%
o35055
10.4%
d23515
 
6.9%
C21883
 
6.5%
i21648
 
6.4%
m20799
 
6.1%
c20098
 
5.9%
f19926
 
5.9%
X7822
 
2.3%
Other values (23)57946
17.1%
Common
ValueCountFrequency (%)
1875
24.7%
654
18.4%
-548
15.4%
2356
10.0%
8217
 
6.1%
4195
 
5.5%
9162
 
4.6%
0161
 
4.5%
5135
 
3.8%
6135
 
3.8%
Other values (2)109
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII341994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e63571
18.6%
n46184
13.5%
o35055
10.3%
d23515
 
6.9%
C21883
 
6.4%
i21648
 
6.3%
m20799
 
6.1%
c20098
 
5.9%
f19926
 
5.8%
X7822
 
2.3%
Other values (35)61493
18.0%

Poidsobj
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.3713156
Minimum0
Maximum9300
Zeros41283
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:08.130327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60
Q3250
95-th percentile400
Maximum9300
Range9300
Interquartile range (IQR)250

Descriptive statistics

Standard deviation247.4794399
Coefficient of variation (CV)1.645788886
Kurtosis209.6776811
Mean150.3713156
Median Absolute Deviation (MAD)60
Skewness8.886271702
Sum20339976
Variance61246.07319
MonotonicityNot monotonic
2023-09-04T17:34:08.229397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041283
30.5%
25013140
 
9.7%
40012939
 
9.6%
306236
 
4.6%
605444
 
4.0%
204938
 
3.7%
254418
 
3.3%
1804290
 
3.2%
1153324
 
2.5%
183181
 
2.4%
Other values (54)36072
26.7%
ValueCountFrequency (%)
041283
30.5%
153
 
< 0.1%
5280
 
0.2%
10254
 
0.2%
1546
 
< 0.1%
163
 
< 0.1%
183181
 
2.4%
204938
 
3.7%
22161
 
0.1%
254418
 
3.3%
ValueCountFrequency (%)
930013
 
< 0.1%
295087
 
0.1%
260077
 
0.1%
2250223
 
0.2%
1680525
 
0.4%
154036
 
< 0.1%
1500163
 
0.1%
1352308
 
0.2%
660177
 
0.1%
5401448
1.1%

points
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)< 0.1%
Missing262
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean12.60023851
Minimum-2500
Maximum360
Zeros20480
Zeros (%)15.1%
Negative21337
Negative (%)15.8%
Memory size1.0 MiB
2023-09-04T17:34:08.313354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2500
5-th percentile-500
Q10
median60
Q380
95-th percentile150
Maximum360
Range2860
Interquartile range (IQR)80

Descriptive statistics

Standard deviation157.7550959
Coefficient of variation (CV)12.52000871
Kurtosis8.371968547
Mean12.60023851
Median Absolute Deviation (MAD)40
Skewness-2.741594474
Sum1701070
Variance24886.67029
MonotonicityNot monotonic
2023-09-04T17:34:08.385354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10020565
15.2%
020480
15.1%
6019880
14.7%
8015057
11.1%
-2010790
8.0%
-50010538
7.8%
15010240
7.6%
407136
 
5.3%
205170
 
3.8%
303872
 
2.9%
Other values (13)11275
8.3%
ValueCountFrequency (%)
-25004
 
< 0.1%
-20003
 
< 0.1%
-15001
 
< 0.1%
-10001
 
< 0.1%
-50010538
7.8%
-2010790
8.0%
020480
15.1%
10307
 
0.2%
152354
 
1.7%
205170
 
3.8%
ValueCountFrequency (%)
360164
 
0.1%
180456
 
0.3%
15010240
7.6%
1201896
 
1.4%
10020565
15.2%
90242
 
0.2%
8015057
11.1%
701743
 
1.3%
6019880
14.7%
502244
 
1.7%

indispobj
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
0
135265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters135265
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0135265
100.0%

Length

2023-09-04T17:34:08.479355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-09-04T17:34:08.574356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0135265
100.0%

Most occurring characters

ValueCountFrequency (%)
0135265
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number135265
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common135265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII135265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135265
100.0%

libcondit
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
Enveloppe Tete de menagere
19982 
Distingo 50 g
12362 
Carton 5 kg
12048 
Distingo 500 g
11767 
Enveloppe Drap de Bain
9598 
Other values (35)
69508 

Length

Max length32
Median length24
Mean length17.36617011
Min length4

Characters and Unicode

Total characters2349035
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarton Tete de menagere
2nd rowCarton Tete de menagere
3rd rowCarton Tete de menagere
4th rowDistingo 500 g
5th rowCarton Tete de menagere

Common Values

ValueCountFrequency (%)
Enveloppe Tete de menagere19982
14.8%
Distingo 50 g12362
 
9.1%
Carton 5 kg12048
 
8.9%
Distingo 500 g11767
 
8.7%
Enveloppe Drap de Bain9598
 
7.1%
Carton Ballon7852
 
5.8%
Carton Drap de Bain7733
 
5.7%
Lettre Suivie 500 g7467
 
5.5%
Distingo 100 g6707
 
5.0%
Lettre Suivie 1 Kg4938
 
3.7%
Other values (30)34811
25.7%

Length

2023-09-04T17:34:08.643354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g53371
 
11.6%
carton45711
 
9.9%
de40037
 
8.7%
distingo31048
 
6.7%
enveloppe30938
 
6.7%
tete22706
 
4.9%
menagere22706
 
4.9%
kg19953
 
4.3%
50019234
 
4.2%
bain17331
 
3.8%
Other values (38)159017
34.4%

Most occurring characters

ValueCountFrequency (%)
326787
13.9%
e273464
 
11.6%
n169351
 
7.2%
i135421
 
5.8%
a134417
 
5.7%
t133425
 
5.7%
o131385
 
5.6%
g127078
 
5.4%
r110854
 
4.7%
092081
 
3.9%
Other values (34)714772
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1574705
67.0%
Space Separator326787
 
13.9%
Uppercase Letter254642
 
10.8%
Decimal Number183169
 
7.8%
Dash Punctuation5503
 
0.2%
Other Punctuation4227
 
0.2%
Math Symbol2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e273464
17.4%
n169351
10.8%
i135421
8.6%
a134417
8.5%
t133425
8.5%
o131385
8.3%
g127078
8.1%
r110854
7.0%
p91242
 
5.8%
l58920
 
3.7%
Other values (11)209148
13.3%
Uppercase Letter
ValueCountFrequency (%)
D49778
19.5%
C45721
18.0%
E35726
14.0%
B25183
9.9%
T24253
9.5%
S18789
 
7.4%
M18200
 
7.1%
L15133
 
5.9%
G6064
 
2.4%
P5473
 
2.1%
Other values (4)10322
 
4.1%
Decimal Number
ValueCountFrequency (%)
092081
50.3%
553625
29.3%
128076
 
15.3%
29175
 
5.0%
3212
 
0.1%
Space Separator
ValueCountFrequency (%)
326787
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5503
100.0%
Other Punctuation
ValueCountFrequency (%)
/4227
100.0%
Math Symbol
ValueCountFrequency (%)
+2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1829347
77.9%
Common519688
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e273464
14.9%
n169351
 
9.3%
i135421
 
7.4%
a134417
 
7.3%
t133425
 
7.3%
o131385
 
7.2%
g127078
 
6.9%
r110854
 
6.1%
p91242
 
5.0%
l58920
 
3.2%
Other values (25)463790
25.4%
Common
ValueCountFrequency (%)
326787
62.9%
092081
 
17.7%
553625
 
10.3%
128076
 
5.4%
29175
 
1.8%
-5503
 
1.1%
/4227
 
0.8%
3212
 
< 0.1%
+2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2348979
> 99.9%
None56
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
326787
13.9%
e273464
 
11.6%
n169351
 
7.2%
i135421
 
5.8%
a134417
 
5.7%
t133425
 
5.7%
o131385
 
5.6%
g127078
 
5.4%
r110854
 
4.7%
092081
 
3.9%
Other values (33)714716
30.4%
None
ValueCountFrequency (%)
û56
100.0%

prixcond
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.44045392
Minimum0
Maximum655
Zeros76766
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:08.724354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334
95-th percentile178
Maximum655
Range655
Interquartile range (IQR)34

Descriptive statistics

Standard deviation112.7080853
Coefficient of variation (CV)2.191039866
Kurtosis11.25986121
Mean51.44045392
Median Absolute Deviation (MAD)0
Skewness3.306616802
Sum6958093
Variance12703.11248
MonotonicityNot monotonic
2023-09-04T17:34:08.799354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
076766
56.8%
13212362
 
9.1%
3411767
 
8.7%
437467
 
5.5%
257372
 
5.5%
1786707
 
5.0%
5454938
 
3.7%
154873
 
3.6%
3151161
 
0.9%
17882
 
0.7%
Other values (6)970
 
0.7%
ValueCountFrequency (%)
076766
56.8%
93
 
< 0.1%
154873
 
3.6%
17882
 
0.7%
257372
 
5.5%
3411767
 
8.7%
35565
 
0.4%
39212
 
0.2%
437467
 
5.5%
45106
 
0.1%
ValueCountFrequency (%)
65574
 
0.1%
5454938
 
3.7%
3151161
 
0.9%
1786707
5.0%
13212362
9.1%
6810
 
< 0.1%
45106
 
0.1%
437467
5.5%
39212
 
0.2%
35565
 
0.4%

puobj
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
0
135265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters135265
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0135265
100.0%

Length

2023-09-04T17:34:08.878354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-09-04T17:34:08.952354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0135265
100.0%

Most occurring characters

ValueCountFrequency (%)
0135265
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number135265
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0135265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common135265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0135265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII135265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0135265
100.0%

annee
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.768255
Minimum2004
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:09.012354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2006
Q12009
median2013
Q32016
95-th percentile2020
Maximum2021
Range17
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.292298526
Coefficient of variation (CV)0.002132534889
Kurtosis-1.066014168
Mean2012.768255
Median Absolute Deviation (MAD)3
Skewness0.1303007879
Sum272257098
Variance18.42382664
MonotonicityNot monotonic
2023-09-04T17:34:09.085386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
201012293
 
9.1%
201110994
 
8.1%
201510685
 
7.9%
201410150
 
7.5%
201610141
 
7.5%
20069911
 
7.3%
20089347
 
6.9%
20128482
 
6.3%
20078245
 
6.1%
20098133
 
6.0%
Other values (8)36884
27.3%
ValueCountFrequency (%)
200410
 
< 0.1%
200527
 
< 0.1%
20069911
7.3%
20078245
6.1%
20089347
6.9%
20098133
6.0%
201012293
9.1%
201110994
8.1%
20128482
6.3%
20136816
5.0%
ValueCountFrequency (%)
20213416
 
2.5%
20206198
4.6%
20196710
5.0%
20186677
4.9%
20177030
5.2%
201610141
7.5%
201510685
7.9%
201410150
7.5%
20136816
5.0%
20128482
6.3%

code_departement
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.82824086
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2023-09-04T17:34:09.181383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q128
median50
Q361
95-th percentile76
Maximum95
Range94
Interquartile range (IQR)33

Descriptive statistics

Standard deviation20.46248459
Coefficient of variation (CV)0.4278326826
Kurtosis-0.8105877476
Mean47.82824086
Median Absolute Deviation (MAD)12
Skewness-0.3032657215
Sum6469487
Variance418.7132757
MonotonicityNot monotonic
2023-09-04T17:34:09.279357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6123996
17.7%
1420595
15.2%
5018342
13.6%
7215230
11.3%
5312498
9.2%
356446
 
4.8%
276365
 
4.7%
495003
 
3.7%
764354
 
3.2%
283712
 
2.7%
Other values (84)18724
13.8%
ValueCountFrequency (%)
15
 
< 0.1%
2231
0.2%
33
 
< 0.1%
412
 
< 0.1%
55
 
< 0.1%
621
 
< 0.1%
73
 
< 0.1%
8153
0.1%
92
 
< 0.1%
1041
 
< 0.1%
ValueCountFrequency (%)
95244
0.2%
94338
0.2%
93311
0.2%
92388
0.3%
91464
0.3%
9017
 
< 0.1%
8950
 
< 0.1%
8836
 
< 0.1%
8769
 
0.1%
86118
 
0.1%

Interactions

2023-09-04T17:34:00.548707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:30.721349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:32.836751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:34.752792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:36.811946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:38.731945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:40.692760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:42.776376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:44.668785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:46.630978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:48.750292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:50.628348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:52.599379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:54.651364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:56.692402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:58.646781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:00.664732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:30.854390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:32.960657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:34.887792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:36.933947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:38.857456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:40.955733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:42.895399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:44.791785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:46.936212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:48.884346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:50.757377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:52.716348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:54.774365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:56.813402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:58.771780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:00.771734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:30.967403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:33.082856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:34.994792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:37.041946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:38.975973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.073372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.003148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:44.914784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.054236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:48.994378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:50.904346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:52.824376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:54.927365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:56.928402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:58.883761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:00.880735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:31.090365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:33.192829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:35.113792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:37.151946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.102969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.194367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.134149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:45.028817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.173236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:49.104346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:51.022347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:52.932347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:55.052365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:57.059406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:59.003751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:00.997705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:31.204642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:33.305852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:35.226791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:37.259945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.216968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.307367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.240148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:45.136785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.286235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:49.222348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:51.131379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:53.045348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:55.167365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:57.205402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:59.128781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:01.117734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:31.330660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:33.431826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:35.350815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:37.383946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.338968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.443370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.359118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:45.295785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.412265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:49.357375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:51.278347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:53.163378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:55.296365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:57.343409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:59.253788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:01.232735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:31.456779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:33.556829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:35.478792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:37.502975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.467997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.568365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.476148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:45.418787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.536236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:49.474348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:51.408347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:53.279394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:55.434365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:57.466917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:59.374752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:01.344735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:31.574775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:33.679827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:35.604792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:37.625949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.582992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.686367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.589126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:45.535812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.658236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:49.589375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:37.876947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.833968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:41.946369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.842784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:45.800785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:47.912239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:49.823348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:51.760377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:34:01.687735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:34.032857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:36.117792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:38.043946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:39.956000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:42.067404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:43.981814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:48.030236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:55.935365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:34:01.800734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:34.158856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:36.233792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:38.160945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:40.073969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:42.188376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:44.100791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:46.036804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:48.159292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:50.057348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:51.998375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:53.878394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:56.057364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:58.079930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:59.967811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:01.909734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:34.268890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:34:00.081818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:40.301971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:42.422404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:48.395292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:50.278375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:52.250375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:54.300365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:33:56.438402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-09-04T17:34:00.316783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:02.253735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:32.718752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:34.629793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:36.701945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:38.621946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:40.546731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:42.665375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:44.558787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:46.516977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:48.640291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:50.509376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:52.489376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:54.536366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:56.571431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:33:58.536930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-04T17:34:00.434734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-09-04T17:34:09.378394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-09-04T17:34:09.550355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-09-04T17:34:09.716355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-09-04T17:34:09.874384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-09-04T17:34:09.982356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-09-04T17:34:02.562735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-04T17:34:03.343424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-04T17:34:04.231078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-09-04T17:34:04.486051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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00446MmeCHRETIENDaniel14540BOURGUEBUS4782004-10-225.04.801.0NaN11382.01.0PoloXL23060.00Carton Tete de menagere00200414
11446MmeCHRETIENDaniel14540BOURGUEBUS4782004-10-225.04.801.0NaN11302.01.0T-shirt BlancL17060.00Carton Tete de menagere00200414
22446MmeCHRETIENDaniel14540BOURGUEBUS4782004-10-225.04.801.0NaN11451.01.0MontreHomme30150.00Carton Tete de menagere00200414
3317860M.VERARDOAnthony35400SAINT MALO212392006-10-030.03.901.01.4511311.01.0T-shirt BlancXL18060.00Distingo 500 g340200635
441330MmeROBERTYvonne61000ALENCON13862004-11-234.06.501.0NaN11423.01.0Tete de menagereConfidence250100.00Carton Tete de menagere00200461
55710MmeADAMMagalie61440MESSEI7542004-11-0410.04.801.0NaN11412.01.0Drap de bainNaN40080.00Carton Poupee00200461
66710MmeADAMMagalie61440MESSEI7542004-11-0410.04.801.0NaN11251.01.0T-shirt Blanc5-6 ans8060.00Carton Poupee00200461
77127M.HARELDaniel76170LILLEBONNE19382004-12-105.16.001.0NaN11414.01.0Drap de bainNaN40080.00Carton Ballon00200476
881881MmeABELSimone27110ECQUETOT19632004-12-104.51.451.0NaN11421.01.0Tete de menagereConfidence250100.00Vide00200427
992847MmeTOURNIEREBernadette50130CHERBOURG EN COTENTIN29902005-01-174.04.001.0NaN11531.01.0Serviette + gantNaN26080.00Lettre Suivie 500 g430200550

Last rows

df_indexcodcligenreclinomcliprenomclicpclivilleclicodcdedatcdetimbreclitimbrecdeNbcolischeqclibarchivebstockcodobjqteColislibobjTailleobjPoidsobjpointsindispobjlibconditprixcondpuobjanneecode_departement
13525513526717840M.BAZARDMichel56400PLUMERGAT900442021-07-260.07.351.07.95111661.01.0Sac IsothermeNaN35090.00Carton 10 kg00202156
13525613526841287MmeDAVIDVeronique14420USSY900452021-07-260.08.801.07.95111661.01.0Sac IsothermeNaN35090.00Carton 10 kg00202114
13525713526941287MmeDAVIDVeronique14420USSY900452021-07-260.08.801.07.9511791.01.0Cle USBNaN2540.00Carton 10 kg00202114
13525813527041287MmeDAVIDVeronique14420USSY900452021-07-260.08.801.07.95111351.01.0Couteaux a SteakNaN38660.00Carton 10 kg00202114
13525913527137510MmeBIDAULTMadeleine14100ST MARTIN DE LA LIEUE900292021-07-210.07.951.07.6011711.01.0Points Bonus FideliteNaN0-500.00Enveloppe Tete de menagere00202114
13526013527239079M. & MmeGERMAINChristian59600ASSEVENT900382021-07-260.07.351.07.35111091.01.0Cloche a camembertNaN54050.00Carton 5 kg00202159
13526113527341291M.GERMAINChristophe24100BERGERAC900472021-07-264.58.801.010.0011422.01.0Tete de menagereConfidence250100.00Lettre Suivie 1 Kg5450202124
13526213527441291M.GERMAINChristophe24100BERGERAC900472021-07-264.58.801.010.0011293.01.0T-shirt BlancM16060.00Lettre Suivie 1 Kg5450202124
13526313527541291M.GERMAINChristophe24100BERGERAC900482021-07-264.27.951.010.0011273.01.0T-shirt Blanc12-14 ans11560.00Lettre Suivie 2 Kg6550202124
13526413527641291M.GERMAINChristophe24100BERGERAC900482021-07-264.27.951.010.0011462.01.0PelucheNaN200100.00Lettre Suivie 2 Kg6550202124